To relax the need for knowing the semantic descriptions a priori, Shao et al. [1]} proposed to learn an embedding space that is discriminative across several source domains to improve generalizability of the PAD model on novel PAs in new domains.
Liu et al. [2]} used similarity between an unknown type of attack with known attack types for zero-shot attack detection. Both approaches analyze data representations in an embedding space and identify new attack types as unfamiliar data points. Our work follows a similar strategy, where novel attack data points are identified as OTDS. We then use the collected OTDS to expand the generalizability of the base model on the identified novel attack types.
